The following relates to video processing systems and methods. More particularly, the following relates to automatic detection of stationary targets in an area of interest and will be described with particular reference thereto. However, it is to be appreciated that the following is applicable to other applications such as, for example, detection of moving targets, changes in the environment, and the like.
Typically, the video system includes an imaging sensor, for example, an electro-optical video camera that provides a sequence of images or frames within a field of view (FOV) of the camera. Intelligent video surveillance systems are often used to automatically detect events of interest such as, for example, potential threats, by detecting, tracking and classifying the targets in the scene. Based on user-defined rules or policies, the intelligent video surveillance systems generate user-alerts if any event in the violation to the user-defined policies is detected. Examples of such events include: monitoring a no parking zone and, for example, initiating an alarm if a car spends more than a certain amount of time in the no parking zone; space management; detecting unattended bags at airports, and other sensitive areas, such as military installations and power plants; detecting the removal of a high value asset, such as an artifact from a museum, an expensive piece of hardware, or a car from a parking lot.
For such applications, the targets that become stationary in the field of view of the imaging sensor need to be detected and classified. One method to detect the stationary targets is to detect moving targets in the area of interest, for example, by employing background subtraction or change detection, e.g., between video frames. For example, a background model is constructed and periodically updated based on a parameter, e.g., a learning rate. Each frame of a video sequence may be registered and compared pixel by pixel to the background model. Pixels that display a substantial difference are considered foreground, or moving, pixels. Pixels that remain unchanged over a pre-specified period of time are considered to be background pixels. In this manner, the moving targets are detected and tracked over time, from one video frame to another. The targets that do not exhibit motion over a user-specified period of time are then deemed stationary targets.
However, this method has limited capabilities. For example, when the area of interest is crowded or has high traffic density, the detection and segmentation of moving targets might provide erroneous results due to frequent occlusions. Another difficulty might arise in tracking a stationary target if other targets move between the stationary target and video camera. It is problematic to determine if the newly detected motion is due to a new target motion or the original stationary target motion.
There is a need for methods and apparatuses that overcome above mentioned difficulties and others.